The ability to act quickly and decisively in today's increasingly competitive marketplace is critical to the success of any organization. The volume of data that is available to organizations is rapidly increasing and frequently overwhelming. The availability of large volumes of data presents various challenges. One challenge is to avoid inundating an individual with unnecessary information. Another challenge is to ensure all relevant information is available in a timely manner.
One known approach to addressing these and other challenges is known as data warehousing. Data warehouses, relational databases, and data marts are becoming important elements of many information delivery systems because they provide a central location where a reconciled version of data extracted from a wide variety of operational systems may be stored. As used herein, a data warehouse should be understood to be an informational database that stores shareable data from one or more operational databases of record, such as one or more transaction-based database systems. A data warehouse typically allows users to tap into a business's vast store of operational data to track and respond to business trends that facilitate forecasting and planning efforts. A data mart may be considered to be a type of data warehouse that focuses on a particular business segment.
Decision support systems have been developed to efficiently retrieve selected information from data warehouses. One type of decision support system is known as an on-line analytical processing system (“OLAP”). In general, OLAP systems analyze the data from a number of different perspectives and support complex analyses against large input data sets.
Typically, business users rely on the above-noted OLAP systems to analyze large volumes of their business information in order to ascertain useful trends and productivity information. The OLAP systems are used to query databases containing the business information and to generate customizable reports which summarize this information.
OLAP systems can be complicated to use and operate. Electronic learning and training systems exist, but such systems are limited to static generic content. It enables a user to experience generic business intelligence training related to concepts and uses. Manual training techniques are also used, but with corporate turnover, the cost of training and retraining hundreds or thousands of users of a business intelligence system can be prohibitive.
Therefore, these and other drawbacks exist with respect to conventional methods of training users on the use of a business intelligence system.